Fine particulate matter (PM 2.5 ) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China o...

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Veröffentlicht in:Atmospheric chemistry and physics Jg. 19; H. 16; S. 11031 - 11041
Hauptverfasser: Zhai, Shixian, Jacob, Daniel J., Wang, Xuan, Shen, Lu, Li, Ke, Zhang, Yuzhong, Gui, Ke, Zhao, Tianliang, Liao, Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Katlenburg-Lindau Copernicus GmbH 29.08.2019
Copernicus Publications
Schlagworte:
2010s (Decade) AD
Air cleanliness
Air pollution
Air pollution control
Air quality
Air quality control
Annual variations
Anomalies
Anthropogenic factors
Carbon monoxide
Coal combustion
Confidence intervals
Councils
Data
Emissions
Emissions (Pollution)
Emissions control
Environmental monitoring
Forecasts and trends
Human-environment interactions
Humidity
Interannual variability
Megacities
Meridional wind
Meteorological research
Meteorology
Nitrogen dioxide
Particulate emissions
Particulate matter
Particulate matter emissions
Particulate pollutants
Particulates
Precipitation
Quality control
Regression analysis
Regression models
Relative humidity
Rivers
Satellites
Statistical analysis
Sulfur
Sulfur dioxide
Sulphur
Sulphur dioxide
Suspended particulate matter
Time series
Trends
Wind
Wind speed
Wind velocities
China
Sichuan Basin
Beijing China
Yangtze River Delta
Pearl River (China)
ISSN:1680-7324, 1680-7316, 1680-7324
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Abstract Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is −4.6 µg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
AbstractList Fine particulate matter (PM 2.5 ) is a severe air pollution problem in China. Observations of PM 2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM 2.5 across China over the 2013–2018 period, averaging at −5.2   µ g m −3  a −1 . Trends in the five megacity cluster regions targeted by the government for air quality control are - 9.3 ± 1.8 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="67e7d5508bfc21be30fd653bf13260f6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00001.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00001.png"/></svg:svg>   µ g m −3  a −1 ( ±95  % confidence interval) for Beijing–Tianjin–Hebei, - 6.1 ± 1.1 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="70e03f9522a113a7dc30ece424d49bce"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00002.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00002.png"/></svg:svg>   µ g m −3  a −1 for the Yangtze River Delta, - 2.7 ± 0.8 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="71b4e427f759f6d47412380862ca6397"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00003.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00003.png"/></svg:svg>   µ g m −3  a −1 for the Pearl River Delta, - 6.7 ± 1.3 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="1fff5043d3a6bcef3a21679e46fa9888"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00004.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00004.png"/></svg:svg>   µ g m −3  a −1 for the Sichuan Basin, and - 6.5 ± 2.5 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="6a19372043f480189dd1e052a5f0d922"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00005.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00005.png"/></svg:svg>   µ g m −3  a −1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide ( SO2 ) and carbon monoxide (CO) show that the declines in PM 2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM 2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM 2.5 trends across China. The MLR model correlates the 10 d PM 2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM 2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM 2.5 decrease across China is −4.6   µ g m −3  a −1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM 2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are - 8.0 ± 1.1 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="a49cc43b6a622598226746c04cd91382"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00006.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00006.png"/></svg:svg>   µ g m −3  a −1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), - 6.3 ± 0.9 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="21c3b8035c0e46ee1bb9baf457471ef6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00007.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00007.png"/></svg:svg>   µ g m −3  a −1 for the Yangtze River Delta (3 % stronger), - 2.2 ± 0.5 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="d7c66650fd7b0217febd6a6b5883da2d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00008.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00008.png"/></svg:svg>   µ g m −3  a −1 for the Pearl River Delta (19 % weaker), - 4.9 ± 0.9 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="d6a4f57abc59de71a45d0091beeca176"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00009.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00009.png"/></svg:svg>   µ g m −3  a −1 for the Sichuan Basin (27 % weaker), and - 5.0 ± 1.9 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="fb5169ee41fe1cecbd62eba02ef83cc4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-11031-2019-ie00010.svg" width="52pt" height="10pt" src="acp-19-11031-2019-ie00010.png"/></svg:svg>   µ g m −3  a −1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM 2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
Fine particulate matter (PM.sub.2.5) is a severe air pollution problem in China. Observations of PM.sub.2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %-50 % decrease in annual mean PM.sub.2.5 across China over the 2013-2018 period, averaging at -5.2 µg m.sup.-3  a.sup.-1 . Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m.sup.-3  a.sup.-1 (±95 % confidence interval) for Beijing-Tianjin-Hebei, -6.1±1.1 µg m.sup.-3  a.sup.-1 for the Yangtze River Delta, -2.7±0.8 µg m.sup.-3  a.sup.-1 for the Pearl River Delta, -6.7±1.3 µg m.sup.-3  a.sup.-1 for the Sichuan Basin, and -6.5±2.5 µg m.sup.-3  a.sup.-1 for the Fenwei Plain (Xi'an). Concurrent 2013-2018 observations of sulfur dioxide (SO.sub.2) and carbon monoxide (CO) show that the declines in PM.sub.2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM.sub.2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM.sub.2.5 trends across China. The MLR model correlates the 10 d PM.sub.2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM.sub.2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM.sub.2.5 decrease across China is -4.6 µg m.sup.-3  a.sup.-1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM.sub.2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m.sup.-3  a.sup.-1 for Beijing-Tianjin-Hebei (14 % weaker than in the original data), -6.3±0.9 µg m.sup.-3  a.sup.-1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m.sup.-3  a.sup.-1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m.sup.-3  a.sup.-1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m.sup.-3  a.sup.-1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015-2017 observations of flattening PM.sub.2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at -5.2 µg m-3 a-1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m-3 a-1 (±95 % confidence interval) for Beijing–Tianjin–Hebei,-6.1±1.1 µg m-3 a-1 for the Yangtze River Delta, -2.7±0.8 µg m-3 a-1 for the Pearl River Delta, -6.7±1.3 µg m-3 a-1 for the Sichuan Basin, and -6.5±2.5 µg m-3 a-1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is -4.6 µg m-3 a-1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m-3 a-1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m-3 a-1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m-3 a-1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m-3 a-1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m-3 a-1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is −4.6 µg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is −4.6 µg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
Audience Academic
Author Zhai, Shixian
Liao, Hong
Zhao, Tianliang
Gui, Ke
Li, Ke
Zhang, Yuzhong
Jacob, Daniel J.
Wang, Xuan
Shen, Lu
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  givenname: Hong
  surname: Liao
  fullname: Liao, Hong
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Snippet Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated...
Fine particulate matter (PM.sub.2.5) is a severe air pollution problem in China. Observations of PM.sub.2.5 have been available since 2013 from a large network...
Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated...
Fine particulate matter (PM 2.5 ) is a severe air pollution problem in China. Observations of PM 2.5 have been available since 2013 from a large network...
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Particulate matter
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Precipitation
Quality control
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Relative humidity
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Satellites
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Sulfur
Sulfur dioxide
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Sulphur dioxide
Suspended particulate matter
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Wind
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Title Fine particulate matter (PM 2.5 ) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology
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https://doaj.org/article/80f404ad44464e04bec089b28d64aac0
Volume 19
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